Cross-disciplinary researchers focusing on connected urban processes, especially those running numerical weather simulations at microscales, require reliable data on building footprints, heights and locations. For example, quantifying the impact of the thermal radiative properties of buildings on urban heating during a heat wave benefits from representation of the 3-dimensional physical characteristics of buildings within the urban location studied. Additionally, understanding how and where urban pollution travels within a city depends on how the buildings from ground level to building tops distort the air flow throughout the city. Even knowledge about which buildings may be susceptible to flooding given the magnitude of a recent rainstorm can be clarified by knowing where buildings are located with respect to the elevation of the earth beneath and surrounding them. As urban micrometeorological modeling helps answer more local scientific questions, high resolution data with wide coverage are needed. Previous research into these issues has yielded useful products for simulating these effects at resolutions of 1 kilometer and coarser; however, no regional-weather-model-readable data products are available at block level resolution for the full extent of any city, county, or wider region. To address this gap, four data sets are presented here: 1) Chicago, 2) Washington, DC, 3) Los Angeles County, and 4) the Arizona “urban corridor.” Parameters include, at 100-meter resolution, frontal area density, plan area density, rooftop area density, plan area fraction, mean building height, standard deviation of building heights, area weighted mean of building heights, building surface area to plan area ratio, height to width ratio, sky view factor and roughness length calculations. These data sets were generated using a new Python tool and validated using statistical and visual methods.